Royal Society for the Protection of Birds

About: Royal Society for the Protection of Birds is a nonprofit organization based out in Sandy, United Kingdom. It is known for research contribution in the topics: Population & Biodiversity. The organization has 670 authors who have published 1425 publications receiving 88006 citations. The organization is also known as: RSPB & Plumage League.

Use of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook

Abstract: Machine learning (ML) has been established and used in science-based applications since the 1970s. The advent and maturation of mathematical algorithms and concepts like Neural Networks, Entropy, Classification and Regression Trees (CARTs), as well as the enhancement of computational power on personal computers worldwide have allowed for the development of many new applications and good approaches to analyzing highly complex systems and their data. Improvements to classical ML techniques, such as boosting, bagging and ensembles have been developed and combined with ML algorithms to yield powerful new tools for both data exploration and analysis (e.g. classification and prediction). Together with the increasing availability of online datasets (public and private), these tools have formed a new ‘science-culture’ that has yet to be fully embraced by the broader scientific community. ML can be used extremely well for data mining and classification, as well as to draw generalizable inference from powerful predictions (Breiman L, Stat Sci 16:199–231 (2001a); Breiman L, Mach Learn J 45:5–32 (2001b)). Thus, it offers a new scientific platform that can help overcome many of the earlier limitations associated with sparse field data, statistical model-fitting, p-values, parsimony (e.g., AIC), Bayesian and post-hoc studies. In contrast to conventional, statistical model-based data analysis, ML usually is non-parametric, so it does not require a priori assumptions about the structure and complexity of a model, nor is it based on just single linear algorithms. This eliminates potential biases and constraints being built into models that result from these assumptions and traditional singular algorithms. In contrast, ML techniques are classification tools of choice and convenience. They can decipher relevant relationships (‘extract the signal’) directly from virtually any data (e.g. messy, ‘gappy’, very large or rather small). Thus, ML can be seen as a new science philosophy with a newly available statistical approach that allows for faster, alternative and more encompassing results that more adequately generalize and reflect the very complex structure of ecological systems. Because ML is not only flexible but efficient, it is an ideal tool for application in the science-based wildlife and conservation management arenas as well as ecology, where decisions need to be robust but time-critical. Here we review some of the advantages and assumed application pitfalls of several key ML algorithms with published examples from the wildlife ecology and biodiversity disciplines using ‘location only’ (presence) data. We then provide a simulation case study to illustrate our key points, and evaluate how ML has the potential to change the way we use information to manage wildlife in times of a rapidly changing global environment and its ongoing crisis.

The seabird wreck in the Bay of Biscay and South-Western Approaches in 2014: A review of reported mortality

Abstract: Between December 2013 and February 2014, a series of storm events occurred in areas of the North Atlantic frequented by migratory seabirds. Prolonged exposure to sustained storm conditions was followed by an unprecedented level of seabird mortality, apparently due to starvation, exhaustion and drowning. A total of 54,982 wrecked birds was recorded along European coastlines of the North-East Atlantic over the winter; 94% of which were dead. The majority of birds found were recorded on the French coastline (79.6%), and the most impacted species was the Atlantic Puffin Fratercula arctica (53.5%). In this paper, we describe the conditions surrounding this wreck event and report the numbers of wrecked and stranded seabirds by combining reports from multiple affected countries.

Correction to: Nest predation and the influence of habitat structure on nest predation of Wood Warbler Phylloscopus sibilatrix, a ground-nesting forest passerine

Abstract: In the original publication of the article, there is a misalignment of the last two columns in table 2. The correct Table 2 is provided below.

WaderMORPH – a user-friendly individual-based model to advise shorebird policy and management

Abstract: Summary: 1.Conservation objectives for non-breeding shorebirds (waders) are determined from their population size. Individual-based models (IBMs) have accurately predicted mortality rate (a determinant of population size) of these species, and are a tool for advising coastal management and policy. However, due to their complexity, the use of these IBMs has been restricted to specialist modellers in the scientific community, whereas, ideally, they should be accessible to non-specialists with a direct interest in coastal issues. 2.We describe how this limitation has been addressed by the development of WaderMORPH, a user-friendly interface to a shorebird IBM, MORPH, that runs within Microsoft Windows. WaderMORPH hides technical and mathematical details of parameterisation from the user and allows models to be parameterised in a series of simple steps. We provide an overview of WaderMORPH and its range of applications. WaderMORPH, its user guide and an example data set can be downloaded from http://individualecology.bournemouth.ac.uk. © 2010 British Ecological Society.